Using Topic Graphs to Understand an Audience
The latest release of PYLON for Facebook Topic Data offers two new targets that return data to build network graphs:
A network graph consists of two elements: nodes and edges. In this case the nodes are Facebook topics and each edge is a connection between any pair of topics that appear together in a single interaction. For instance, if Facebook infers that the author of a post is talking about both "BMW" and "skiing" the...
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Announcing PYLON 1.7.1 - Introducing Enhanced Sampling Support
Today we released version 1.7.1 of PYLON for Facebook Topic Data. This release includes some key features that will make it easier for you to build richer analysis results with PYLON.
If you try to record a large audience with PYLON (such as people discussing a box office movie launch) you can quickly hit recording limits, especially if you're sharing your allowances across your end customers. Sampling allows you to record a proportion of interactions that is representative for...
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Studying large audiences with PYLON for Facebook topic data
Release 1.7.1 of PYLON for Facebook Topic Data introduced a number of new features. One key feature is the additional sampling options you now have at your disposal when looking to study large audiences.
Analyzing large audiences inside recording limits
As a PYLON customer you have a daily recording allowance which you cannot exceed. If you are looking to study a large audience, such as the audience engaging with a global brand, you can quickly hit your recording limits.
Sampling allows you...
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New Tokenized Targets for PYLON Query Filters
In this blog I'm going to look at tokenized targets in query filters. If you're new to the PYLON platform take a look at our PYLON 101 and Get Started guides.
PYLON offers two types of CSDL filter:
An interaction filter takes data from Facebook, filters it, and records the result to an index. For instance, you might write an interaction filter to sift through Facebook topic data looking for stories...
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Filter Swapping (part 2)
Use Case: Hashtags
In part 1 of this blog I introduced a new feature of PYLON called filter swapping. In this part I'll look at a use case that reflects a real-world use case.
Suppose you want to monitor a sector and find the top hashtags associated with it. Then you want to take those top hashtags and see how they're used in a wider context, across the whole of Facebook, not just in connection with the sector you chose. However, you know that the list of hashtags is likely to change...
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